The invention relates to time series analysis, and in particular to determining a periodic cycle of time series data. Apart from identification and estimation of a stable and accurate model, time series modeling consists of calculating some periodic cycles or seasonality patterns which are comprised in the time series data. For such analyses it is known in the art to use additional seasonally-lagged regressors in an equation (seasonal autoregressive integrated moving average (ARIMA) models), introducing additional dummy variables into some trend equation before the (non-)linear estimation of its coefficients, or iterative correction of the forecasted value with respect to its recent seasonal counterpart (exponential smoothing).
In seasonal data, there is a cyclical data pattern. For example, quarterly gross domestic product (GDP) data are characterized by some decline in the winter quarter (with less activity in construction and agriculture sector), and some decline as summer holiday effect. It can be expected a cycle of four observations, on top of an upward-sloping trend and variable-length business cycle. Monthly unemployment data are characterized by a cycle of twelve, with some activity decline in the winter and school graduates entering the labor market in the summer. Heaviness of traffic is possibly to be determined by three cycles, e.g. daily: rush hours and night, weekly: no rush hours on the week-end and annual: long week-ends, national holidays, summer holidays etc. The exact cycle lengths depend here on time intervals of measurement.
A problem may arise when this cycle length is not known (or not so obvious) in advance. Another question is whether the seasonality of the data is significant at all. If not, introducing seasonal parameters into the model could account for over fitting the data. The time series analysis methods developed as part of e.g. Intelligent Miner time series functionality do support versions both with and without seasonality, but it is still needed to decide whether to use it and what the cycle length is.
State of the art time series analysis software, e.g. SPSS, Statistica, Eviews, Stata and the like, normally requires seasonality cycle length, e.g., twelve for monthly data, as an input parameter. Besides, the user is often asked whether his data are quarterly, monthly etc. and the program sets then the parameter to four, twelve, etc., respectively. Other programs also provide tools graphically supporting an expert user when taking decision as to seasonality length.